How entity resolution powers accurate business verification—techniques, implementation strategies, and why it's the foundation of effective KYB.
Entity resolution is the technical foundation of effective Know Your Business (KYB) verification. It's the process of determining when different records refer to the same real-world business—connecting "GTL Services LLC" in Delaware's registry to "Green Thumb Landscaping" on a merchant application to "GREEN THUMB LANDSCAPE" in payment processor records.
Without entity resolution, KYB verification fails at scale. With sophisticated resolution, businesses sail through verification while fraudulent applications get flagged. This guide explains how entity resolution works, why it matters for KYB, and what separates basic matching from production-grade resolution.
Business information exists across thousands of sources, each with its own format, naming conventions, and data quality:
State filing: "GTL Services LLC" Trade name filing: "Green Thumb Landscaping" Payment processor: "GREEN THUMB LANDSCAPE" Google Business Profile: "Green Thumb Landscaping & Lawn Care" Credit bureau: "GTL SERVICES"
These are all the same business. But how does a system know that?
Legal names vs. trade names: Businesses register legally as "XYZ Holdings LLC" but operate publicly as "Joe's Pizza"
Abbreviations: "Corporation" becomes "Corp" or "Co"; "Limited Liability Company" becomes "LLC" or "L.L.C."
Stylization: "McDonald's" vs "McDonalds" vs "MCDONALDS"
Typos and data entry errors: "Acme" becomes "Acmee" or "Acne"
Evolution: Business names change through rebranding, acquisition, or legal restructuring
Name is just one attribute. Entity resolution must also handle:
Address variations:
Identifier inconsistencies:
Corporate structures:
The core KYB question is: "Is this business legitimate?" Answering requires matching the application to authoritative records.
Consider a business applying for a merchant account as "Green Thumb Landscaping" at "456 Main St, Columbus OH." The Secretary of State record shows "GTL Services LLC" registered at "1209 Orange St, Wilmington DE."
Without entity resolution: No match found → manual review or rejection
With entity resolution: Match found with high confidence (trade name filing links to legal entity, operating address differs from registered address as expected) → auto-approval
Entity resolution determines whether legitimate businesses pass verification or get stuck in manual review queues.
Straight-through processing (STP) rates measure how many applications resolve automatically without human intervention. Entity resolution directly impacts STP:
Exact match only: 30-40%
Basic fuzzy matching: 50-60%
Advanced multi-attribute: 70-80%
Graph-based with enrichment: 80-90%
The difference between 40% and 80% STP is the difference between a sustainable operation and one buried in manual review backlogs.
Entity resolution reveals patterns invisible to record-by-record analysis:
Shell company detection: Multiple businesses at the same registered agent address, sharing the same formation date and officer, despite claiming to be independent
Fraud rings: Applications with different business names but connected through shared addresses, phones, or beneficial owners
Sanctions evasion: An entity with a slightly misspelled name that would otherwise match a sanctioned party
Serial fraud: An individual appearing as the beneficial owner of multiple failed businesses
Tracing ownership requires connecting entities through ownership chains:
Application: "Green Thumb Landscaping"
↓ resolution
Legal Entity: "GTL Services LLC" (Delaware)
↓ ownership lookup
Parent: "Smith Holdings LLC" (Wyoming)
↓ ownership lookup
Beneficial Owner: "Jane Smith" (person)
Without entity resolution, ownership verification stops at the legal entity name on the application.
Match records using exact values of unique identifiers:
Identifiers used:
Example:
Application EIN: 12-3456789
Registry EIN: 12-3456789
→ Exact match
Strengths:
Limitations:
Deterministic matching is the starting point but insufficient alone—it typically matches only 20-40% of records.
Compare multiple attributes using similarity algorithms and weighted scoring:
Name similarity algorithms:
Example:
Application name: "Green Thumb Landscaping LLC"
Registry name: "GTL Services LLC"
Trade name: "Green Thumb Landscaping"
Name similarity: Low (0.3)
Trade name similarity: High (0.95)
Address similarity: Medium (0.7)
→ Weighted score: 0.82 → Match
Address standardization:
Weighted scoring:
Match score =
(name_sim × 0.35) +
(address_sim × 0.25) +
(city_state × 0.15) +
(identifier × 0.25)
Threshold tuning is critical: too low creates false positives, too high creates false negatives.
Train models on labeled match/non-match pairs to learn complex patterns:
Supervised learning:
Benefits:
Challenges:
Connect records through relationships, not just attribute similarity:
Relationship types:
Transitive connections:
Record A shares address with Record B
Record B shares officer with Record C
→ A may be related to C (transitive link)
Graph analysis:
Graph-based resolution excels at:
1. Data Ingestion
2. Normalization
3. Blocking/Indexing
4. Comparison
5. Classification
6. Clustering
7. API/Output
False positive vs. false negative tradeoffs:
For KYB, false negatives (missing legitimate matches) are often more costly than false positives (flagging matches that need review). Tune accordingly, but monitor both.
Threshold calibration:
New businesses: Recently formed entities may not appear in all data sources yet. Use formation documents plus initial signals.
Sole proprietors: May have no state filing at all. Match on individual identity plus business signals (trade name if registered, address, web presence).
Franchises: Same brand, different legal entities. Match to correct franchisee entity, not franchisor.
Name changes: Historical names may still appear in some sources. Maintain name history and match against all known names.
International entities: Different identifier types, character sets, and registry structures. Jurisdiction-aware resolution.
Precision: Of records the system says match, what percentage actually match?
Precision = True Positives / (True Positives + False Positives)
Recall: Of records that actually match, what percentage does the system find?
Recall = True Positives / (True Positives + False Negatives)
F1 Score: Harmonic mean balancing precision and recall
F1 = 2 × (Precision × Recall) / (Precision + Recall)
STP Rate: Percentage of applications resolved without manual review
Match Rate: Percentage of applications successfully matched to authoritative records
Review Yield: Percentage of manual reviews that result in different decisions than the automated suggestion
Time to Decision: How long from application submission to verification decision